The brain scan could predict the success of spinal surgery for chronic pain

The brain scan could predict the success of spinal surgery for chronic pain

A 10-minute brain scan can predict the effectiveness of risky spinal surgery to relieve intractable pain. The Kobe University finding gives doctors a much-needed biomarker to discuss with patients considering spinal cord stimulation.

For patients with chronic pain that cannot be cured by other means, a surgical procedure called “spinal cord stimulation” is considered a method of last resort. The treatment involves implanting it into the spine of patients and electrically stimulating the spinal cord. Because the spinal cord transmits signals from throughout the body to the brain, the position of the leads is adjusted so that patients feel the stimulation at the site of pain. Kobe University anesthetist Ueno Kyohei says: "A big problem is that the procedure is effective for some but not other patients, and this is usually evaluated in a short trial of a few days to two weeks before permanent implantation. Although this trial is short, it is still an invasive and risky procedure.

Functional magnetic resonance imaging (FMRI) has become a standard tool for visualizing how the brain processes information. More specifically, it can show which parts of the brain are active in response to a stimulus and thus which regions are functionally connected. “In a previous study, we reported that for the analgesic ketamine, pain relief was negatively correlated with how strongly two regions of the default mode network are connected before drug administration,” explains Ueno. The default mode network, which plays an important role in self-focused thinking, has previously been implicated in chronic pain. Another relevant factor is how the default mode network connects to the salience network, which is involved in regulating attention and responding to stimuli. Ueno says: "Therefore, we wanted to investigate whether the correlation of activities within and between these networks could be used to predict the response to spinal cord stimulation."

He and his team published their results in theBritish Journal of Anesthesia. They found that the better patients responded to spinal cord stimulation therapy, the weaker a specific area of ​​the default mode network was connected to one in the salience network. Ueno comments: "This not only provides an attractive biomarker for prognostication of treatment effectiveness, but also the idea that aberrant connectivity between these networks is primarily responsible for the development of intractable chronic pain."

An fMRI scan is not the only option. The combination of pain questionnaires with various clinical indices has been reported as another similarly reliable predictor of patient response to spinal cord stimulation. However, the researchers write, "Although the cost of an MRI scan is controversial, both patients and providers will benefit if responsiveness to spinal cord stimulation can be predicted by a 10-minute resting-state FMRI scan."

A total of 29 patients with various forms of intractable chronic pain took part in this study at Kobe University. On the one hand, this diversity is likely why the overall response to treatment was lower compared to similar studies in the past, and also made it more difficult to accurately assess the relationship between brain function and responsiveness. On the other hand, the researchers also say that

From a clinical perspective, the ability to predict outcomes can provide significant benefit for patients with various diseases. We believe that a more accurate assessment will become possible in the future with more cases and more research. We are also currently conducting research into which brain regions are strongly influenced by different patterns of spinal cord stimulation. At this point we are only at the beginning of this research, but our main goal is to use functional brain imaging as a biomarker for spinal cord stimulation therapy to identify the optimal treatment for each patient in the future. “

Ueno Kyohei, an anesthesiologist at Kobe University

This research was funded by the Japan Society for the Advancement of Science (Grant 21K08993). It was carried out in collaboration with a researcher from Ritsumeikan University.

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